The Coordinated Regional Climate Downscaling Experiment (CORDEX) Southeast Asia further downscaled three of its 25 km products, i.e. EC-Earth, HadGEM2-ES and MPI-ESM-MR forced regional climate simulations to a higher resolution of 5 km. A newer RegCM4.7 model was used for this exercise. For the simulations over Peninsular Malaysia, analysis shows that these simulations have much smaller precipitation biases. They can correctly predict the annual precipitation cycle over the east coast of Peninsular Malaysia compared to the 25 km simulations. The ensemble of these 5 km simulations indicates that Peninsular Malaysia is expected to experience a decrease in its seasonal mean precipitation, regardless of the RCP 4.5 or RCP 8.5 scenarios. Aside from the reduction of seasonal mean precipitation, consecutive dry and wet days are expected to increase and decrease, respectively. This indicates that Peninsular Malaysia will experience a long dry spell in the future. At the same time, it is suggested that Peninsular Malaysia will have fewer days with very heavy precipitation. Overall, the findings from this study suggest that the 5 km downscaled climate simulations improve significantly over 25 km and that the Peninsular Malaysia region can expect a drier future climate and extremes.

A multi-model ensemble from the new CMIP6 models was utilized to determine the future changes in precipitation over Southeast Asia (SEA; longitude: 90°E–140°E, latitude: 15°S–30°N). The changes are computed for the three (3) future time slices (2021–2040, 2041–2060, and 2081–2100) under four (4) different scenarios based on the Shared Socioeconomic Pathways (SSPs): 1-2.6, 2-4.5, 3-7.0, and 5-8.5. Our results indicate that future rainfall in the SEA-averaged region could increase by about 4%, 5%, 6%, and 9% towards the end of the century relative to the present-day average (1995–2014) under SSP1-2.6, 2-4.5, 3-7.0, and 5-8.5, respectively. Among all scenarios, SSP3-7.0 widely shows remarkably dry conditions whereas SSP5-8.5 suggests extremely wet conditions on different time scales. A clear dissociation of wet and dry areas is expected in the far-term period (2081–2100). Changes in the annual cycle indicate that monsoon rainfall could experience significant increases. The study also emphasizes the importance of moisture flux convergence (MFC) in determining precipitation patterns across different seasons and regions. The results suggest that MFC plays a crucial role in the projected increase or decrease of rainfall in SEA regions. Spatial correlation of future global mean temperature (GMT) and rainfall have a high positive (negative) correlation in the north (south) latitudes. Changes in rainfall are found to be sensitive to GMT. The responses to future rainfall changes per degree Celsius of warming are at the rate of 8.9%, 6.3%, 3.6%, and 2.7% under SSP1-2.6, 2-4.5, 3-7.0, and 5-8.5, respectively.

This study investigates how to use the gridded satellite datasets of observational precipitation to improve the performance of the climatological simulation by using the method of non-parametric quantile mapping (QM). The precipitation in Southeast Asia is simulated in 2001–2005 using the climate model of Weather Research and Forecasting (WRF). Two satellite datasets of observational precipitation, GSMaP and CHIRPS, are used for model training, simulation evaluation, and cross-validation. The evaluations of simulation and bias correction suggest that QM is able to perfectly correct the overall quantile distributions of the simulated precipitation, which is characterized by overestimation at most quantiles, especially for light and extreme precipitation. After the QM correction based on GSMaP (CHIRPS), the relative bias of the monthly average for all months is reduced from 39.3% to 4.1% (from 57.2% to 4.2%). The biases of spatial patterns are largely narrowed from 43.5% (59.4%) to 4.0% (2.5%) for annual-mean precipitation and from 43.5% (59.4%) to 4.0% (2.5%) for extreme precipitation. The results indicate that the QM correction based on the gridded satellite datasets outperforms the raw model output and greatly improves the estimates of the simulated precipitation.

Simulations of seven regional climate models (RCMs) from CORDEX-SEA were bias-corrected using the quantile mapping (QM) method to examine the possible ranges of future mean precipitation and extreme index changes over Southeast Asia (SEA). Eleven rainfall indices including total precipitation (PRCPTOT), maximum one day precipitation (RX1day), the maximum number of consecutive dry days (MCDD), intensity of very wet days (R95pTOT) and the frequency of heavy rainfall days (R20mm) were considered. The QM procedure largely reduced the biases and inter-model variations in the historical period. For future projections, some QM modifications can be found but these were season and location dependent. The projected large changes of mean seasonal precipitation, especially over Indochina, were slightly magnified after the correction. Generally, the projected bias-corrected ensemble mean (ENSMEAN) indicated increased mean rainfall during boreal winters in mainland SEA by as much as 30% depending on the projection periods. During the boreal summer, mean rainfall in the maritime continent was projected to decrease by as much as -30% in some areas in Indonesia. For extreme precipitation, the bias-corrected projected extreme indices showed a possible range of changes between -40% and +60%, depending on type of indices, season, location, scenarios, and period of projections.